Many pathogenic and commensal organisms are multidrug resistant due to exposure to various antibiotics. Often, this antimicrobial resistance is encoded by integrons that occur on plasmids or that are integrated into the bacterial chromosome. Integrons are commonly associated with bacterial genera in the family Enterobacteriaceae. We determined that class 1 integrases were present in approximately 46% of the isolates from the family Enterobacteriaceae; class 2 integrases were present only among Escherichia coli and Salmonella isolates. Seven percent of veterinary isolates were positive for class 3 integrase by DNA-DNA hybridization but could not be confirmed to be positive by PCR. None of the veterinary isolates possessed the class 4 integrase gene. The distribution of these integrase genes was variable within the members of the family Enterobacteriaceae when some or all integrase classes were absent from a particular genus. There was also considerable variability in the distribution of these integrases within a species, depending on the animal host. Unlike the class 1 integrases, the other integrase class, intI2, appears to be more restricted in its distribution among the members of the family Enterobacteriaceae. There is also considerable variability in the distribution of the class 1 integrases within E. coli strains isolated from different food animals. The class 1 integrases are the most widely disseminated of the four classes among the members of the family Enterobacteriaceae from both the clinical and normal flora of animals. This is the first report to closely examine the distribution of class 2 integrases in members of the family Enterobacteriaceae isolated in the United States.

Wearable, multisensor, consumer devices that estimate sleep are now commonplace, but the algorithms used by these devices to score sleep are not open source, and the raw sensor data is rarely accessible for external use. As a result, these devices are limited in their usefulness for clinical and research applications, despite holding much promise. We used a mobile application of our own creation to collect raw acceleration data and heart rate from the Apple Watch worn by participants undergoing polysomnography, as well as during the ambulatory period preceding in lab testing. Using this data, we compared the contributions of multiple features (motion, local standard deviation in heart rate, and “clock proxy”) to performance across several classifiers. Best performance was achieved using neural nets, though the differences across classifiers were generally small. For sleep-wake classification, our method scored 90% of epochs correctly, with 59.6% of true wake epochs (specificity) and 93% of true sleep epochs (sensitivity) scored correctly. Accuracy for differentiating wake, NREM sleep, and REM sleep was approximately 72% when all features were used. We generalized our results by testing the models trained on Apple Watch data using data from the Multi-ethnic Study of Atherosclerosis (MESA), and found that we were able to predict sleep with performance comparable to testing on our own dataset. This study demonstrates, for the first time, the ability to analyze raw acceleration and heart rate data from a ubiquitous wearable device with accepted, disclosed mathematical methods to improve accuracy of sleep and sleep stage prediction.

Journal of Clinical Sleep Medicine is dedicated to advancing the science of clinical sleep medicine. In order to provide subscribers with access to new scientific developments as early as possible, accepted papers are posted prior to their final publication in an issue. These papers are posted as received-without copyediting or formatting by the publisher. In some instances, substantial changes are made during the copyediting and formatting processes; therefore, the final version of the paper may differ significantly from this version. Unless indicated otherwise, all papers are copyright of the American Academy of Sleep Medicine. No paper in whole or in part may be used in any form without written permission from the American Academy of Sleep Medicine.

Consumer sleep technologies (CSTs) are widespread applications and devices that purport to measure and even improve sleep. Sleep clinicians may frequently encounter CST in practice and, despite lack of validation against gold standard polysomnography, familiarity with these devices has become a patient expectation. This American Academy of Sleep Medicine position statement details the disadvantages and potential benefits of CSTs and provides guidance when approaching patient-generated health data from CSTs in a clinical setting. Given the lack of validation and United States Food and Drug Administration (FDA) clearance, CSTs cannot be utilized for the diagnosis and/or treatment of sleep disorders at this time. However, CSTs may be utilized to enhance the patient-clinician interaction when presented in the context of an appropriate clinical evaluation. The ubiquitous nature of CSTs may further sleep research and practice. However, future validation, access to raw data and algorithms, and FDA oversight are needed.

No abstract

Fifty-eight women with anorexia or bulimia nervosa were compared with 24 normal women on measures of defense style and parental bonding. Results indicated that all eating-disorder subtypes exhibit more primitive defenses and fewer mature ones than controls. Eating-disorder patients uniformly recalled less paternal empathy than controls. Thus, difficulties involving object representations of fathers may be a theme common to eating disorders. No major differences were identified among eating-disorder subtypes, suggesting that these disorders share substantial psychodynamic features. Patterns of parental bonding were associated with defense styles in a manner consistent with theories that link defense style development to early object relationships.Recent findings suggest a substantial "interface" between eating and personality disorders. Many studies have found eating-disorder (ED) patients who binge and purge (whether they be anorexic or normal in weight) to exhibit "borderline" personality traits: among them mood lability, self-destructiveness, and other impulse-dysregulation problems. Conversely, anorexic patients who solely restrict food intake (without binging) tend to be overcontrolled or "ob-

From smart work scheduling to optimal drug timing, there is enormous potential in translating circadian rhythms research results for precision medicine in the real world. However, the pursuit of such effort requires the ability to accurately estimate circadian phase outside of the laboratory. One approach is to predict circadian phase non-invasively using light and activity measurements and mathematical models of the human circadian clock. Most mathematical models take light as an input and predict the effect of light on the human circadian system. However, consumer-grade wearables that are already owned by millions of individuals record activity instead of light, which prompts an evaluation of the accuracy of predicting circadian phase using motion alone. Here, we evaluate the ability of four different models of the human circadian clock to estimate circadian phase from data acquired by wrist-worn wearable devices. Multiple datasets across populations with varying degrees of circadian disruption were used for generalizability. Though the models we test yield similar predictions, analysis of data from 27 shift workers with high levels of circadian disruption shows that activity, which is recorded in almost every wearable device, is better at predicting circadian phase than measured light levels from wrist-worn devices when processed by mathematical models. In those living under normal living conditions, circadian phase can typically be predicted to within 1 hour, even with data from a widely available commercial device (the Apple Watch). These results show that circadian phase can be predicted using existing data passively collected by millions of individuals with comparable accuracy to much more invasive and expensive methods.

To evaluate various psychological constructs used in formulations of anorexia and bulimia nervosa, we compared 76 eating‐disordered, 20 psychiatric, and 24 normal women on measures of irrational cognitions, object‐relations disturbances, and defense styles. The eating‐disordered groups exhibited more disturbance on all measures than normals and many pathological elevations relative to psychiatric controls. Despite these differences, common qualitative features were identified in all patient groups, suggesting that formulations based on the factors examined alone, while useful in providing an understanding of patients' issues, will be inadequate to account for eating‐disorder development.